Assignment 28 - 4 March 2023 : Divya Pardeshi

Q1. Load the "titanic" dataset using the load_dataset function of seaborn. Use Plotly express to plot a scatter plot for age and fare columns in the titanic dataset.

Ans.

#importing the required libraries
import seaborn as sns
import plotly.express as px

# Load the titanic dataset
titanic_data = sns.load_dataset("titanic")

titanic_data.shape

(891, 15)

titanic_data.head()

	survived	pclass	sex	age	sibsp	parch	fare	embarked	class	who	adult_male	deck	embark_town	alive	alone
0	0	3	male	22.0	1	0	7.2500	S	Third	man	True	NaN	Southampton	no	False
1	1	1	female	38.0	1	0	71.2833	C	First	woman	False	C	Cherbourg	yes	False
2	1	3	female	26.0	0	0	7.9250	S	Third	woman	False	NaN	Southampton	yes	True
3	1	1	female	35.0	1	0	53.1000	S	First	woman	False	C	Southampton	yes	False
4	0	3	male	35.0	0	0	8.0500	S	Third	man	True	NaN	Southampton	no	True

# Create the scatter plot using Plotly Express
fig = px.scatter(titanic_data, x="age", y="fare")

# Show the scatter plot
fig.show()

Q2. Using the tips dataset in the Plotly library, plot a box plot using Plotly express.

Ans.

import plotly.express as px

# Load the tips dataset
tips_data = px.data.tips()

tips_data.shape

(244, 7)

tips_data.head()

	total_bill	tip	sex	smoker	day	time	size
0	16.99	1.01	Female	No	Sun	Dinner	2
1	10.34	1.66	Male	No	Sun	Dinner	3
2	21.01	3.50	Male	No	Sun	Dinner	3
3	23.68	3.31	Male	No	Sun	Dinner	2
4	24.59	3.61	Female	No	Sun	Dinner	4

# Create the box plot using Plotly Express
fig = px.box(tips_data, x="day", y="total_bill")

# Show the box plot
fig.show()

Q3. Using the tips dataset in the Plotly library, Plot a histogram for x= "sex" and y="total_bill" column in the tips dataset. Also, use the "smoker" column with the pattern_shape parameter and the "day" column with the color parameter.

Ans.

import plotly.express as px

# Load the tips dataset
tips_data = px.data.tips()

tips_data.shape

(244, 7)

tips_data.head()

	total_bill	tip	sex	smoker	day	time	size
0	16.99	1.01	Female	No	Sun	Dinner	2
1	10.34	1.66	Male	No	Sun	Dinner	3
2	21.01	3.50	Male	No	Sun	Dinner	3
3	23.68	3.31	Male	No	Sun	Dinner	2
4	24.59	3.61	Female	No	Sun	Dinner	4

# Create the histogram using Plotly Express
fig = px.histogram(tips_data, x="sex", y="total_bill", color="day", pattern_shape="smoker")

# Show the histogram
fig.show()

Q4. Using the iris dataset in the Plotly library, Plot a scatter matrix plot, using the "species" column for the color parameter. Note: Use "sepal_length", "sepal_width", "petal_length", "petal_width" columns only with the dimensions parameter.

Ans.

import plotly.express as px

# Load the iris dataset
iris_data = px.data.iris()

iris_data.shape

(150, 6)

iris_data.head()

	sepal_length	sepal_width	petal_length	petal_width	species	species_id
0	5.1	3.5	1.4	0.2	setosa	1
1	4.9	3.0	1.4	0.2	setosa	1
2	4.7	3.2	1.3	0.2	setosa	1
3	4.6	3.1	1.5	0.2	setosa	1
4	5.0	3.6	1.4	0.2	setosa	1

# Create the scatter matrix plot using Plotly Express
fig = px.scatter_matrix(iris_data, dimensions=["sepal_width", "sepal_length", "petal_width", "petal_length"], color="species")

# Show the scatter matrix plot
fig.show()

C:\Users\swati\anaconda3\lib\site-packages\plotly\express\_core.py:279: FutureWarning:

iteritems is deprecated and will be removed in a future version. Use .items instead.

Q5. What is Distplot? Using Plotly express, plot a distplot.

Ans.

A Distplot or distribution plot, depicts the variation in the data distribution. Seaborn Distplot represents the overall distribution of continuous data variables. The Seaborn module along with the Matplotlib module is used to depict the distplot with different variations in it. The Distplot depicts the data by a histogram and a line in combination to it.

import plotly.express as px
import numpy as np

# Create some data
data = np.random.randn(1000)

# Create the distplot
fig = px.histogram(data, marginal="rug")

# Add a title
fig.update_layout(title_text="Distribution of Data")

# Show the plot
fig.show()

We can also plot distplot using below code:

import plotly.express as px
import numpy as np
import scipy.stats as stats

# Create a sample dataset
np.random.seed(2)
data = np.random.randn(1000)

# Create the histogram
fig = px.histogram(data, nbins=30, histnorm='probability density')

# Add the KDE curve
x_values = np.linspace(data.min(), data.max(), 100)
y_values = stats.gaussian_kde(data).pdf(x_values)
fig.add_scatter(x=x_values, y=y_values, mode='lines', name='KDE')

# Update the layout
fig.update_layout(title='Distribution Plot')

# Show the plot
fig.show()